Social Boosted Recommendation With Folded Bipartite Network Embedding

Chen, H; Yin, H; Chen, T; Wang, W; Li, X; Hu, X

Social Boosted Recommendation With Folded Bipartite Network Embedding

Chen, H

Yin, H Chen, T Wang, W Li, X Hu, X

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Publisher:: IEEE COMPUTER SOC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2022, 34, (2), pp. 914-926
Issue Date:: 2022-02-01

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Field	Value	Language
dc.contributor.author	Chen, H https://orcid.org/0000-0001-7963-8813
dc.contributor.author	Yin, H
dc.contributor.author	Chen, T
dc.contributor.author	Wang, W
dc.contributor.author	Li, X
dc.contributor.author	Hu, X
dc.date.accessioned	2023-04-05T01:04:55Z
dc.date.available	2023-04-05T01:04:55Z
dc.date.issued	2022-02-01
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2022, 34, (2), pp. 914-926
dc.identifier.issn	1041-4347
dc.identifier.issn	1558-2191
dc.identifier.uri	http://hdl.handle.net/10453/169125
dc.description.abstract	With the prevalence of online social platforms, social recommendation has emerged as a promising direction that leverages the social network among users to enhance recommendation performance. However, the available social relations among users are usually extremely sparse and noisy, which may lead to inferior recommendation performance. To alleviate this problem, this paper novelly exploits the implicit higher-order social influence and dependencies among users to enhance social recommendation. In this paper, we propose a novel embedding method for general bipartite graphs, which defines inter-class message passing between explicit relations and intra-class message passing between implicit higher-order relations via a novel sequential modelling paradigm. Inspired by recent advances in self-attention-based sequential modelling, the proposed model features a self-attentive representation learning mechanism for implicit user-user relations. Moreover, this paper also explores the inductive embedding learning for social recommendation problems to improve the recommendation performance in cold-start settings. The proposed inductive learning paradigm for social recommendation enables embedding inference for those cold-start users and items (unseen during training) as long as they are linked to existing nodes in the original network. Extensive experiments on real-world datasets demonstrate the superiority of our method and suggest that higher-order implicit relationship among users is beneficial to improving social recommendation.
dc.language	English
dc.publisher	IEEE COMPUTER SOC
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering
dc.relation.isbasedon	10.1109/TKDE.2020.2982878
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Information Systems
dc.title	Social Boosted Recommendation With Folded Bipartite Network Embedding
dc.type	Journal Article
utslib.citation.volume	34
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-05T01:04:54Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	34
utslib.citation.issue	2

Abstract:

With the prevalence of online social platforms, social recommendation has emerged as a promising direction that leverages the social network among users to enhance recommendation performance. However, the available social relations among users are usually extremely sparse and noisy, which may lead to inferior recommendation performance. To alleviate this problem, this paper novelly exploits the implicit higher-order social influence and dependencies among users to enhance social recommendation. In this paper, we propose a novel embedding method for general bipartite graphs, which defines inter-class message passing between explicit relations and intra-class message passing between implicit higher-order relations via a novel sequential modelling paradigm. Inspired by recent advances in self-attention-based sequential modelling, the proposed model features a self-attentive representation learning mechanism for implicit user-user relations. Moreover, this paper also explores the inductive embedding learning for social recommendation problems to improve the recommendation performance in cold-start settings. The proposed inductive learning paradigm for social recommendation enables embedding inference for those cold-start users and items (unseen during training) as long as they are linked to existing nodes in the original network. Extensive experiments on real-world datasets demonstrate the superiority of our method and suggest that higher-order implicit relationship among users is beneficial to improving social recommendation.

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http://hdl.handle.net/10453/169125